Controlling radiating elements

ABSTRACT

An apparatus, method and computer program product is disclosed. The apparatus may comprise means for receiving a performance metric for an antenna array comprised of a plurality of radiating elements, the performance metric being based on performance data associated with the antenna array, the antenna array having a radiating configuration represented by configuration parameters. The apparatus may also comprise means for updating the configuration parameters dependent on the received performance metric by means of estimating new configuration parameters for moving the performance metric towards a target value. The apparatus may also comprise means for re-configuring the radiating configuration of the antenna array based on the updated configuration parameters such that the physical geometry of the antenna array is changed.

FIELD

Example embodiments relate to methods, systems and computer programs forcontrolling one or more radiating elements, for example the radiatingelements of one or more antennas which may form part of an antennaarray.

BACKGROUND

An antenna may comprise one or more radiating elements which are fedwith the same radio frequency signal. An antenna array may comprisemultiple such antennas with individual radio frequency chains. Antennaarrays are used for various purposes, for example for beamforming intelecommunications networks. Radiating elements may also be used forreceiving radio frequency energy.

SUMMARY

A first aspect may provide an apparatus, comprising: means for receivinga performance metric for an antenna array comprised of a plurality ofradiating elements, the performance metric being based on performancedata associated with the antenna array, the antenna array having aradiating configuration represented by configuration parameters; meansfor updating the configuration parameters dependent on the receivedperformance metric by means of estimating new configuration parametersfor moving the performance metric towards a target value; means forre-configuring the radiating configuration of the antenna array based onthe updated configuration parameters such that the physical geometry ofthe antenna array is changed.

The re-configuring means may be configured to change the physicalgeometry by moving one or more radiating elements based on there-configured configuration.

The updating means may be further configured to iteratively update theconfiguration parameters dependent on received performance metricsresulting from the updated configuration parameters, until a stopcondition is reached.

The stop condition may correspond to a fixed number of updateiterations.

The stop condition may correspond to the performance metric reaching thetarget value. The stop condition may correspond to a fixed number ofupdate iterations for which the performance metric does not move towardsthe target value.

The updating means may comprise a machine learning model configured toestimate, based on stored training data, updated configurationparameters likely to move the performance metric towards the targetvalue. The machine learning model may be trained with data derived frominitial connections quality and subsequently updating the model based onthe results caused by the updated configuration parameters. The machinelearning model may be trained with initial configuration parameters andsubsequently updated based on the results of re-configurations caused bythe updated configuration parameters. The machine learning model may betrained from a digital sibling.

The performance metric may be received from a computing means configuredto receive performance data from a physical node of at least part of acommunications network with which the antenna array forms part.

The computing means may be associated with a base station of at leastpart of a cellular communications network with which the antenna arrayforms part.

The performance metric may be received from a simulation means whichsimulates a physical node of at least part of a communications networkwith which the antenna array forms part.

The simulated physical node may be associated a base station of at leastpart of a cellular communications network with which the antenna arrayforms part.

The computing or simulation means may comprise a part of the apparatus.

The received performance metric may represent the measured or simulatedvalue of one or more of data throughput, call drop rate, outageprobability and energy consumption associated with the antenna array.

The performance metric may represent said measured or simulated valuesfor each of a different number of user equipment within a cell of acellular communications system associated with the antenna array.

The configuration parameters may comprise one or more of, for a j^(th)radiating element of an i^(th) antenna of the antenna array:

Position x_(i, j) ϵ 

 _(i, j)⊂ 

 ³ Azimuth angle φ_(i, j) ϵ[0, 2π] Elevation angle θ_(i, j)ϵ[−π/2, π/2]Analog phase shift β_(i, j)ϵ[−π, π] Analog signal gainα_(i, j)ϵ[α_(min), α_(max)]and the means for re-configuring the radiating configuration of theantenna array may be configured to update one or more of saidconfiguration parameters to effect a corresponding change at one or moreof the radiating elements of the antenna array.

The means for re-configuring the radiating configuration of the antennaarray may be configured to perform the re-configuring in substantiallyreal-time or near real-time based on the last-received value of theperformance metric.

Another aspect may provide a method, comprising: receiving a performancemetric for an antenna array comprised of a plurality of radiatingelements, the antenna array having a radiating configuration representedby configuration parameters; updating the configuration parametersdependent on the received performance metric by means of estimating newconfiguration parameters for moving the performance metric towards atarget value; and re-configuring the radiating configuration of theantenna array based on the updated configuration parameters such thatthe physical geometry of the antenna array is changed.

The re-configuring may change the physical geometry by moving one ormore radiating elements based on the re-configured configuration.

The method may further comprise iteratively updating the configurationparameters dependent on received performance metrics resulting from theupdated configuration parameters, until a stop condition is reached.

The stop condition may correspond to a fixed number of updateiterations. The stop condition may correspond to the performance metricreaching the target value. The stop condition may correspond to a fixednumber of update iterations for which the performance metric does notmove towards the target value.

The updating may comprise using a machine learning model configured toestimate, based on stored training data, updated configurationparameters likely to move the performance metric towards a target value.

The method may further comprise training the machine learning model withdata derived from initial connections quality and subsequently updatedbased on the results caused by the updated configuration parameters. Themethod may further comprise training the machine learning model withinitial configuration parameters and subsequently updating based on theresults of re-configurations caused by the updated configurationparameters.

The machine learning model may be trained with data from a digitalsibling.

The performance metric may be received from a computing means configuredto receive performance data from a physical node of at least part of acommunications network with which the antenna array forms part.

The computing means may be associated with a base station of at leastpart of a cellular communications network with which the antenna arrayforms part.

The performance metric may be received from a simulation means whichsimulates a physical node of at least part of a communications networkwith which the antenna array forms part.

The simulated physical node may be associated a base station of at leastpart of a cellular communications network with which the antenna arrayforms part.

The computing or simulation means may comprise a part of the apparatus.

The received performance metric may represent the measured or simulatedvalue of one or more of data throughput, call drop rate, outageprobability and energy consumption associated with the antenna array.

The performance metric may represent said measured or simulated valuesfor each of a different number of user equipment within a cell of acellular communications system associated with the antenna array.

The configuration parameters may comprise one or more of, for a j^(th)radiating element of an i^(th) antenna of the antenna array:

Position x_(i, j) ϵ 

 _(i, j)⊂ 

 ³ Azimuth angle φ_(i, j) ϵ[0, 2π] Elevation angle θ_(i, j)ϵ[−π/2, π/2]Analog phase shift β_(i, j)ϵ[−π, π] Analog signal gainα_(i, j)ϵ[α_(min), α_(max)]and the re-configuring of the radiating configuration of the antennaarray may comprise updating one or more of said configuration parametersto effect a corresponding change at one or more of the radiatingelements of the antenna array.

The re-configuring of the radiating configuration of the antenna arraymay comprise performing the re-configuring in substantially real-time ornear real-time based on the last-received value of the performancemetric.

Another aspect may provide an apparatus comprising at least one, atleast one memory directly connected to the at least one processor, theat least one memory including computer program code, and the at leastone processor, with the at least one memory and the computer programcode being arranged to perform the method of: receiving a performancemetric for an antenna array comprised of a plurality of radiatingelements, the antenna array having a radiating configuration representedby configuration parameters; updating the configuration parametersdependent on the received performance metric by means of estimating newconfiguration parameters for moving the performance metric towards atarget value; and re-configuring the radiating configuration of theantenna array based on the updated configuration parameters such thatthe physical geometry of the antenna array is changed.

The at least one processor, with the at least one memory and thecomputer code are further arranged to perform the method of any abovedefinition connected with the above aspect.

Another aspect may provide a computer program product comprising a setof instructions which, when executed on an apparatus, are configured tocause the apparatus to carry out the method of: receiving a performancemetric for an antenna array comprised of a plurality of radiatingelements, the antenna array having a radiating configuration representedby configuration parameters; updating the configuration parametersdependent on the received performance metric by means of estimating newconfiguration parameters for moving the performance metric towards atarget value; and re-configuring the radiating configuration of theantenna array based on the updated configuration parameters such thatthe physical geometry of the antenna array is changed.

Another aspect provides a non-transitory computer readable mediumcomprising program instructions stored thereon for performing a method,comprising: receiving a performance metric for an antenna arraycomprised of a plurality of radiating elements, the antenna array havinga radiating configuration represented by configuration parameters;updating the configuration parameters dependent on the receivedperformance metric by means of estimating new configuration parametersfor moving the performance metric towards a target value; andre-configuring the radiating configuration of the antenna array based onthe updated configuration parameters such that the physical geometry ofthe antenna array is changed.

Another aspect may provide an apparatus comprising: at least oneprocessor; and at least one memory including computer program codewhich, when executed by the at least one processor, causes theapparatus: to receive a performance metric for an antenna arraycomprised of a plurality of radiating elements, the antenna array havinga radiating configuration represented by configuration parameters; toupdate the configuration parameters dependent on the receivedperformance metric by means of estimating new configuration parametersfor moving the performance metric towards a target value; and tore-configure the radiating configuration of the antenna array based onthe updated configuration parameters such that the physical geometry ofthe antenna array is changed.

Reference to a “means” above may refer to any of hardware, software,electrical or electronic circuitry, or any combination thereof,configured to perform the stated functions.

DRAWINGS

Example embodiments will now be described by way of non-limitingexample, with reference to the accompanying drawings, in which:

FIG. 1 is a schematic view of an antenna array;

FIG. 2 is a block diagram of a system comprising an antenna array andcontroller in accordance with example embodiments;

FIG. 3 is a partial schematic view of another antenna array,controllable in accordance with the controller in FIG. 2;

FIG. 4 is a partial schematic view of another antenna array,controllable in accordance with the controller in FIG. 2;

FIG. 5 is a partial schematic view of another antenna array,controllable in accordance with the controller in FIG. 2;

FIG. 6 is a block diagram of a further system comprising an antennaarray and controller in accordance with example embodiments;

FIG. 7 is a block diagram showing a real control system and digitalsibling system for use in accordance with example embodiments;

FIG. 8 is a flow diagram showing operations that may be performed inaccordance with example embodiments;

FIG. 9 is a further flow diagram showing operations that may beperformed in accordance with example embodiments;

FIG. 10 is a further flow diagram showing operations that may beperformed in accordance with example embodiments;

FIG. 11 is a further flow diagram showing operations that may beperformed in accordance with example embodiments;

FIG. 12 is a further flow diagram showing operations that may beperformed in accordance with example embodiments;

FIG. 13 is a block diagram showing components of the FIG. 2 or FIG. 6 inaccordance with example embodiments; and

FIG. 14 shows a non-volatile media according to some embodiments.

DETAILED DESCRIPTION

Example embodiments relate to controlling one or more radiatingelements, for example of one or more radio frequency antennas. Exampleembodiments may relate to an apparatus, method and/or computer programfor controlling one or more antenna radiating elements (hereafter“radiating elements”) of one or more antennas of an antenna array; anantenna array is a physical apparatus which comprises a plurality ofdistinct antennas. The antennas of the array may work together,effectively as a single antenna, to transmit or receive radio frequencywaves. Antenna arrays are used in various fields, for example incellular communications.

Example embodiments may relate to adaptively controlling the radiatingelements of one or more antennas. The adaptive control may be performedin real-time or near real-time to provide on-the-fly modifications to acurrent antenna array configuration based on, for example, current orvery recent conditions, such as network conditions, the number of userequipment in one or more cells associated with the antenna array, and soon. These examples are not to be considered limiting on the scope of thepresent disclosure and are given merely by way of example. The antennaarray configuration may refer to characteristics of individual antennas,and/or characteristics of individual radiating elements thereof, interms of their phase, gain, and positional characteristics, for example.Modifications to the antenna array configuration may be for the purposeof modifying radiation or receiving characteristics of one or moreradiating elements. All or a subset of the radiating elements may bere-configured in this way.

Referring to FIG. 1, an antenna array to is shown in schematic view. Theantenna array to comprises a plurality of antennas 12-15. Each of theantennas 12-15 has respective radio frequency inputs and outputs 16-19.Each of the antennas 12-15 may comprise one or more radiating elements20, which for example may be dipoles, which may be fed by the same radiofrequency signal via the radio frequency inputs and outputs 16-19. Eachantenna 12-15 may be associated with a respective radio frequencyprocessing chain.

Some characteristics of an antenna array are antenna spacing and totalsize (relative to the wavelength) which is known as the aperture. Thesize may determine the directivity of the array, that is its ability tofocus radiated energy towards certain directions. The number of antennasmay determine the radiated/received energy.

Referring to FIG. 2, the radiating elements 20 of each antenna 12-15 mayshare in general a single analog-to-digital converter(ADC)/digital-to-analog converter (DAC) 30. However, the phase andamplitude (or gain) of the radio frequency signals of each radiatingelement 20 can optionally be individually controlled in the analogdomain, as indicated by the phase shifter and amplifier elements 22.Phase and amplitude are example characteristics of a given radiatingelement 20.

In some embodiments, other characteristics of a given radiating element20 may comprise one or more of position, azimuth angle and elevationangle. For this purpose, mechanical positioning means may be associatedwith each radiating w element 20. An example mechanical positioningmeans may comprise a positional actuator 24 (“PA”). A plurality of suchactuators 24 may be used, for example linear and/or rotationalpositional actuators, for modifying one or more of the position, azimuthangle and elevation angles.

Embodiments herein may provide a controller 26 for controlling theconfiguration of one or more the radiating elements 20 by means ofcontrolling one or more respective phase shifter and amplifier elements22 and/or positional actuators 24. In FIG. 2, the controller 26 is shownconnected to only one such phase shifter and amplifier element 22 and/orpositional actuator 24 but it will be appreciated that, in practice, thecontroller 26 may be connected to each device it is to control.

In overview, example embodiments may involve the controller 26 (oranother processing device associated with the controller) receiving aperformance metric L(Ω) for an antenna array to comprised of a pluralityof radiating elements 20. The performance metric L(Ω) may be based onperformance data associated with the antenna array to, the antenna arrayhaving a radiating configuration represented by configurationparameters. In some embodiments, the configuration parameters maycorrespond to one or more of the above characteristics of givenradiating elements 20. For example, the jth radiating element of the ithantenna of the antenna array to may the following configurationparameters:

Position x_(i, j) ϵ 

 _(i, j)⊂ 

 ³ Azimuth angle φ_(i, j) ϵ[0, 2π] Elevation angle θ_(i, j)ϵ[−π/2, π/2]Analog phase shift β_(i, j)ϵ[−π, π] Analog signal gainα_(i, j)ϵ[α_(min), α_(max)]

The values of the configuration parameters may reflect the settings ofindividual radiating elements 20 in correspondence to the configuration.

The configuration parameters may be combined into a parameter vector:

ωi,j=[x ^(T) ,ϕi,j,θi,j,βi,j,αi,j]^(T) ∈R ⁷.

The antenna array 10 may have M antennas, where the ith antenna has Niradiating elements. The set of all configuration parameters of theantenna array 10 may be denoted by

Ω={ωi,j:i=1, . . . ,M:j=1, . . . ,Ni}.

Embodiments may also comprise updating the configuration parameters Ωdependent on the received performance metric L(Ω), and re-configuringthe radiating configuration of the antenna array 10 based on the updatedconfiguration parameters {tilde over (Ω)}¹. The values of the updatedconfiguration parameters {tilde over (Ω)}¹ therefore reflect updatedsettings to be applied to the individual radiating elements 20. This maybe achieved for example by one or more of increasing or decreasing thephase shift ai,j, the signal gain ai,j, and modifying via the positionalactuators 24 the position xi,j, azimuth angle ϕi,j and elevation angleθi,j of the individual radiating elements 20.

The performance metric L(Ω) may be an arbitrary metric. For example, theperformance metric L(Ω) may be a value representative of datathroughput, e.g. cell sum throughput, 5^(th) percentile throughput,median throughput or geometric throughput. For example, the performancemetric L(Ω) may be a value representative of one or more of call droprate, outage probability, energy consumption and so on. One or more ofthe above examples may be the basis of computing a performance metricL(Ω), which is a value indicative of performance of the one or moreperformance characteristics.

By modifying one or more configuration parameters, the performancemetric L(Ω) is likely to change. Therefore, embodiments may involvemodifying the one or more configuration parameters Ω with the aim ofmoving the performance metric L(Ω) towards a target. The target mayrepresent an optimal condition, e.g. solving the optimisation problem:

arg max L(Ω)  (1)

but some other target, usually in the direction of improving technicalperformance, may be used.

In some embodiments, it is proposed to use reinforcement learning (RL)and/or supervised learning (SL) methods to adapt the positions, and/orother controllable characteristics, of the radiating elements 20 withthe aim of improving, if not maximising, the performance metric L(Ω). Inthis way, embodiments enable changing of the geometry an antenna array10, on-the-fly, such that it can be automatically improved or optimizedfor any radio environment, without human interaction. The main benefitsare improved performance, as well as reduced capital and ongoingexpenditure.

The performance metric L(Ω) may itself either be computed frommeasurements based on real data associated with the antenna array 10, orfrom simulations. The performance metric L(Ω) may also be available inexplicit mathematical form.

FIG. 3 shows in schematic view part of another antenna array 30 whichcomprises an antenna having twelve radiating elements 32. As indicatedby the arrows 34 shown relative to one such radiating element 32, eachradiating element may be moved in terms of position xi,j by anassociated positional actuator.

FIG. 4 shows in perspective view part of another antenna array which maycomprise an antenna 36 comprising a mast 37 and a plurality of radiatingelements 38 extending therefrom. As indicated, the mast 37 may berotated by a positional actuator, and each radiating element 38 may bemodified in terms of angle with respect to the mast, the length of eacharm and rotation about its own axis.

FIG. 5 shows in top plan view part of another antenna array 50,particularly a planar antenna array, which comprises four antennacolumns 51-54, each comprising two blocks 55, 56 of four antennas orradiating elements 57. As indicated by the arrows, the vertical positionof each block 55, 56 of each antenna column 51-54 may be controllablymoved. In another embodiment, the horizontal position of each block 55,56 may be controllable, either additionally or alternatively. Thecontrollable movement may be by means of any method described above.

A controllable antenna array according to example embodiments maycombine two or more of the FIG. 3-FIG. 4 characteristic modifications,whether wholly or in part.

There will now be described examples of how control is achieved.

In overview, control may be performed using hardware, software or acombination thereof.

FIG. 6 is a block diagram showing functional modules of a controller 60,which is shown in association with an antenna array 62 and connectedbase station 64. The base station 64 may for example be a cellular basestation, commonly referred to as a nodeB (NB), enhanced nodeB (eNB) ornext-generation nodeB (gNB) depending on the standard used, althoughpresent embodiments are not standard specific. The base station 64 maybe associated with a cell of a cellular communications network withinwhich may be located a plurality of user equipments 66. The controller60 may comprise a learning algorithm module 68 and an antenna arraycontroller 70.

The learning algorithm may be configured to produce an antenna arrayconfiguration {tilde over (Ω)}^(t) which is subsequently applied to theantenna array 62 through the antenna array controller 70.

The base station may be configured to measure the performance metricL({tilde over (Ω)}^(t)), for example the throughput achieved by the userequipments 66 within its cell, which is subsequently forwarded to thelearning algorithm 68, which in turn is configured to produce an updatedantenna configuration and so on which re-configures the characteristicsof the antenna array 62.

FIG. 8 is a flow diagram showing processing operations of a generalisedmethod of example embodiments. A first operation 801 comprises receivinga performance metric for an antenna array. The performance metric may bereceived from a remote node, such as from a base station such as thebase station 64. A second operation 802 comprises updating configurationparameters for the antenna array 62 dependent on the receivedperformance metric. A third operation 803 comprises re-configuring theradiating configuration of the antenna based on the updatedconfiguration parameters.

A more specific, but still general learning algorithm which works forany performance metric L(Ω) will now be described, relating to thehigh-level view provided in FIG. 6. A corresponding flowchart, which mayrepresent processing operations, is provided in FIG. 9 which is nowreferred to. The reference numerals are not necessarily indicative ofprocessing order.

In a first operation 901, we let t=0 and choose a set of initialparameters Ω^(t).

In another operation 902 we compute a perturbed configuration {tildeover (Ω)}^(t). For example, for i=1, . . . , M, j=1, . . . , Ni, arandom vector εi,j may be drawn from some distribution p(ε). Forexample, p(ε) could be a multivariate normal distribution with zero meanand a fixed covariance matrix σ²I, i.e., p(ε)=N(0, σ²I). We could alsohave a different distribution pi,j (ε) for each radiating element of theantenna array 62.

In another operation 903, the antenna array 62 may be configuredaccording to:

{tilde over (ω)}^(t) _(i,j)=ω^(t) _(i,j) +εi,j,i=1, . . . ,M,j=1, . . .,N _(i).  (2)

This defines the parameters {tilde over (Ω)}^(t).

In another operation 904, the performance metric L({tilde over (Ω)}^(t))is computed, e.g. measured.

In another operation 905, it is determined if a stop condition isreached. If not, another operation 906 may comprise computing parameterupdates according to:

ω_(i,j) ^(t+1)=ω_(i,j) ^(t) +ηL({tilde over (Ω)}^(t))∇_(ω) _(i,j) _(t)log(p({tilde over (ω)}_(i,j) ^(t)|ω_(i,j) ^(t))),i=1, . . . ,M,j=1, . .. N _(i)   (3)

for some learning rate η>0. For p(ε)=N(0, σ⁻²I), this boils down to:

$\begin{matrix}{\omega_{i,j}^{t + 1} = {\omega_{i,j}^{t} + {\eta\;{L\left( {\overset{\sim}{\Omega}}^{t} \right)}\frac{\left( {\omega_{i,j}^{t} - {\overset{\sim}{\omega}}_{i,j}^{t}} \right)}{\sigma^{2}}}}} & (4)\end{matrix}$

In the same or a different operation, the value oft is updated to +1,and the process may return to operation 902.

The initial antenna array configuration Ω^(t) may be obtained fromsimulations. In this case, the performance metric L(Ω) is not measuredfrom a real-world system, e.g. data received from the antenna array 62,but simulated on a computer. Having good initialization is helpful tospeed-up the learning process. However, it is only optional and notrequired for realizing embodiments herein.

In some embodiments, if the updated parameter vectors ω^(t+1) or {tildeover (ω)}^(t+1) are determined to comprise non-feasible values, forexample if they correspond to a radiating element position that is notphysically possible, the parameter vectors may be projected back to aset of feasible parameter vectors. For example, entries of the vectormay be clipped to certain minimum and/or maximum values.

Alternatively, the learning rate q may be adjusted such that i, jremains feasible.

In some embodiments, operations 903 and 904 may be run multiple times tocompute performance metrics for different perturbations of the sameantenna configuration. In this case, the gradient computed in operation906 may be averaged over these values.

In operation 906, the learning-rate η may be computed and adapted by anystochastic gradient descent (SGD) algorithm, e.g., ADAM, RMSProp,Momentum, to give some examples.

The stop criterion in operation 905 may take multiple forms. Forexample, the process may stop after a fixed number of trainingiterations, or stop when L({tilde over (Ω)}^(t)) has not improved duringa fixed number of iterations, or stop when L({tilde over (Ω)}^(t)) hasreached a desired value. In some embodiments, there may be no stopoperation 905, i.e. the training may run indefinitely.

The second term of equation (3) is also known as the policy gradient.Various methods to improve the convergence speed of this algorithm existin the (deep) learning literature, see, e.g. R. S. Sutton, D.McAllester, S. Singh, and Y. Mansour, Policy gradient methods forreinforcement learning with function approximation, Proceedings of the12th International Conference on Neural Information Processing Systems,NIPS, pages 1057-1063, Cambridge, Mass., USA, 1999. MIT Press.

A learning algorithm for an differentiable performance metric L(Ω) willnow be described.

In some cases, the gradient ∇ωi,j L(Ω) may be explicitly calculated.This may be the case when L(Ω) is either computedanalytically/numerically or through simulations.

Referring to the flow diagram of FIG. 10, processing steps will now bedescribed by way of example. Note that other numerical optimizationalgorithms can be used in this case.

In a first operation 1001, we let t=0 and choose a set of initialparameters Ω^(t).

In another operation 1002, we configure the antenna array 62 accordingto Ω^(t).

In another operation 1003, we computer the performance metric L(Ω^(t)).

In another operation 1004, it is determined if a stop condition isreached. If not, another operation 1005 may comprise computing parameterupdates according to:

ω_(i,j) ^(t+1)=ω_(i,j) ^(t)+η∇_(ω) _(i,j) _(t) L(Ω^(t)),i=1, . . .,M,j=1, . . . N _(i)  (5)

for a learning rate η>0.

In the same or a different operation, the value oft is updated to andthe process may return to operation 1002.

Similar to some other embodiments, if the updated parameter vectorsω^(t+1) or {tilde over (ω)}^(t+1) are determined to comprisenon-feasible values, for example if they correspond to a radiatingelement position that is not physically possible, the parameter vectorsmay be projected back to a set of feasible parameter vectors. Forexample, entries of the vector may be clipped to certain minimum and/ormaximum values.

The learning-rate η may be computed and adapted by any stochasticgradient descent (SGD) algorithm, e.g., ADAM, RMSProp, Momentum, to givesome examples.

In some embodiments, operations 1002 and 1003 may be run multiple timesto compute performance metrics for different perturbations of the sameantenna configuration. In this case, the gradient computed in operation1006 may be averaged over these values.

In some embodiments, the above-mentioned initial configuration of theantenna array 62 can be obtained by simulation. That is, the algorithmsdescribed above may be used to find parameters Ω through simulationswhich are then used as initial parameters Ω° for the algorithm runningon a real system. This is schematically shown in FIG. 7, comprising areal system 80A, for example that shown in FIG. 6, and a digital siblingsystem 80B, a term widely used to mean a realistic simulator of a systemthat it is desired to improve or optimise. A digital sibling system 80Bmay run simulations faster than real-time and significantly increase thespeed of the training process. In other embodiments, initial parametersfrom another base station may be used, sometimes referred to as“transfer learning.”

So far, the antenna configuration parameters are optimized with respectto a given performance metric L(Ω^(t)). However, it may be possible thatthere are other parameters s∈R^(P) which should be explicitly taken intoaccount. These parameters may comprise, for example, the number of userequipments 66 in a cell, the time of the day, weather conditions, etc.

In some examples, a goal is to find a mapping Ω=ƒ(s) from theseadditional parameters to the optimal antenna array configuration thatmaximizes the performance metric

L(Ω,s).

In this case, we may assume that the function ƒ has tunable parametersμ, i.e., ƒ(s)=ƒμ(s), and we would like to solve the optimizationproblem:

arg max L(ƒμ(s),s).  (6)

μ

We may obtain the following algorithm for non-differentiable performancemetrics L(ƒμ(s), s). A flow diagram showing processing operations inthis case is shown in FIG. 11.

In a first operation 1101, we let t=0 and choose a set of initialparameters μ^(t). We also compute Ω^(t)=ƒμt(s).

In another operation 1102, we compute a perturbed configuration {tildeover (Ω)}^(t). This may comprise taking the parameters s and computingΩ^(t)=ƒμt(s). For i=1, . . . , M, j=1, . . . , Ni, we may draw a randomvector εi,j from some distribution p(ε). For example, p(ε) could be amultivariate normal distribution with zero mean and fixed covariancematrix σ²I, i.e., p(ε)=N(0, σ²I). We may also have a differentdistribution pi,j(ε) for each radiating element of the antenna array.

In another operation 1103, we may configure the antenna array 62according to:

{tilde over (ω)}_(i,j) ^(t)=ω_(i,j) ^(t)+ε_(i,j) ,i=1, . . . ,M,j=1, . .. N _(i)  (7)

This defines the parameters {tilde over (Ω)}^(t).

In another operation 1104, the performance metric L({tilde over(Ω)}^(t)) is computed, e.g. measured.

In another operation 1105, it is determined if a stop condition isreached. If not, another operation 1106 may comprise computing parameterupdates according to:

μ^(t+1)=μ^(t) +ηL({tilde over (Ω)}^(t) ,s)∇_(μ) ₂ log(p({tilde over(ω)}_(i,j) ^(t)|ω_(i,j) ^(t))),i=1, . . . ,M,j=1, . . . N _(i)   (8)

for a learning rate η>0.

In the same or a different operation, the value oft is updated to t+1,and the process may return to operation 1102.

In some embodiments, operations 1103 and 1104 may be run multiple timesto compute performance metrics for different perturbations of the sameantenna configuration. In this case, the gradient computed in operation1106 may be averaged over these values.

For differentiable performance metrics L(ƒμ(s), s) we propose thefollowing method, the processing operations of which are shown in FIG.12.

In a first operation 1201, we let t=0 and choose a set of initialparameters μ^(t). We also compute Ω^(t)=ƒμt(s).

In another operation 1202, we compute a perturbed configuration {tildeover (Ω)}^(t). This may comprise taking the parameters sand computingΩ^(t)=ƒμt(s).

In another operation 1203, we may configure the antenna array 62according to Ω^(t)=ƒμt(s).

In another operation 1204, we may compute performance metric L(Ω^(t),s).

In another operation 1205, it is determined if a stop condition isreached. If not, another operation 1206 may comprise computing parameterupdates according to:

μ^(t+1)=μ^(t)+η∇_(μ) _(i,j) _(t) L(Ω^(t) ,s),i=1, . . . ,M,j=1, . . . N_(i)  (9)

for a learning rate η>0.

In the same or a different operation, the value oft is updated to t+1,and the process may return to operation 1202.

In some embodiments, operations 1203 and 1204 may be run multiple timesto compute performance metrics for different perturbations of the sameantenna configuration. In this case, the gradient computed in operation1206 may be averaged over these values.

In some embodiments, the mapping ƒμ(s) may be performed using a neuralnetwork (NN) and μ may denote its trainable parameters, e.g., itsweights and/or biases.

In some embodiments, if the updated parameter vectors ω^(t+1) or {tildeover (ω)}^(t+1) are determined to comprise non-feasible values, forexample if they correspond to a radiating element position that is notphysically possible, the parameter vectors may be projected back to aset of feasible parameter vectors. For example, entries of the vectormay be clipped to certain minimum and/or maximum values. Alternatively,the learning rate η can be adjusted such that ω^(t+1) always remainsfeasible. Another way is to “punish” the learning system may be byfeedback of a predefined negative loss when a non-feasible setting ischosen, so that by minimizing the loss, the systems “learns” to avoidsuch configurations.

The learning algorithm may employ a machine learning model, which may beinitialised with data derived from initial connections quality, e.g.signal quality, and subsequently updated based on the results caused bythe updated configuration parameters. The machine learning model inother embodiments may be initialised with initial configurationparameters.

FIG. 14 shows an apparatus according to an embodiment. The apparatus mayprovide the controller functionality shown in FIG. 2 and/or the controlfunctionality shown in FIG. 60. The apparatus comprises at least oneprocessor 420 and at least one memory 410 directly or closely connectedto the processor. The memory 410 includes at least one random accessmemory (RAM) 410 b and at least one read-only memory (ROM) 410 a.Computer program code (software) 415 is stored in the ROM 410 a. Theapparatus may be connected to a TX path, for example to the antennaarray 62, and a RX path, for example from the base station 64 in orderto transmit configuration data and receive the performance metric alongrespective paths. The apparatus may be connected with a user interfaceUI for instructing the apparatus and/or for outputting results. The atleast one processor 420, with the at least one memory 410 and thecomputer program code 415 may be arranged to cause the apparatus to atleast perform at least the method according to one or more of FIGS. 9 to12.

FIG. 15 shows a non-transitory media according to some embodiments. Thenon-transitory media is a computer readable storage medium. It may bee.g. a CD, a DVD, a USB stick, a blue ray disk, etc. The non-transitorymedia stores computer program code causing an apparatus to perform themethod of one or more of FIGS. 9 to 12 when executed by a processor suchas processor 420 of the apparatus of FIG. 14. The memory may be volatileor non-volatile. It may be e.g. a RAM, SRAM, a flash memory, a FPGAblock ram, a DCD, a CD, a USB stick, and a blue ray disk. If nototherwise stated or otherwise made clear from the context, the statementthat two entities are different means that they perform differentfunctions. It does not necessarily mean that they are based on differenthardware. That is, each of the entities described in the presentdescription may be based on a different hardware, or some or all of theentities may be based on the same hardware. It does not necessarily meanthat they are based on different software. That is, each of the entitiesdescribed in the present description may be based on different software,or some or all of the entities may be based on the same software. Eachof the entities described in the present description may be embodied inthe cloud.

Implementations of any of the above described blocks, apparatuses,systems, techniques or methods include, as non-limiting examples,implementations as hardware, software, firmware, special purposecircuits or logic, general purpose hardware or controller or othercomputing devices, or some combination thereof. Some embodiments may beimplemented in the cloud.

It is to be understood that what is described above is what is presentlyconsidered the preferred embodiments. However, it should be noted thatthe description of the preferred embodiments is given by way of exampleonly and that various modifications may be made without departing fromthe scope as defined by the appended claims.

1-44. (canceled)
 45. An apparatus comprising: at least one processor;and at least one memory including computer program code which, whenexecuted by the at least one processor, causes the apparatus to performacts comprising: receiving a performance metric for an antenna arraycomprised of a plurality of radiating elements, the antenna array havinga radiating configuration represented by configuration parameters;updating the configuration parameters dependent on the receivedperformance metric by estimating new configuration parameters for movingthe performance metric towards a target value; and re-configuring theradiating configuration of the antenna array based on the updatedconfiguration parameters such that the physical geometry of the antennaarray is changed.
 46. The apparatus of claim 45, wherein there-configuring the radiating configuration comprises changing thephysical geometry by moving one or more radiating elements based on there-configured configuration.
 47. The apparatus of claim 45, wherein theupdating the configuration parameters comprises iteratively update theconfiguration parameters dependent on received performance metricsresulting from the updated configuration parameters, until a stopcondition is reached, wherein the stop condition comprises one of afixed number of update iterations, the performance metric reaching thetarget value, a fixed number of update iterations for which theperformance metric does not move towards the target value.
 48. Theapparatus of claim 45, wherein the updating the configuration parameterscomprises a machine learning model configured to estimate, based onstored training data, updated configuration parameters likely to movethe performance metric towards the target value.
 49. The apparatus ofclaim 48, wherein the machine learning model is trained with dataderived from initial connections quality and subsequently updated basedon the results caused by the updated configuration parameters.
 50. Theapparatus of claim 48, wherein the machine learning model is trainedwith initial configuration parameters and subsequently updated based onthe results of re-configurations caused by the updated configurationparameters.
 51. The apparatus of claim 49, wherein the machine learningmodel is trained using a digital sibling.
 52. The apparatus of claim 45,wherein the performance metric is received from a computing processorconfigured to receive performance data from a physical node of at leastpart of a communications network with which the antenna array formspart.
 53. The apparatus of claim 52, wherein the computing processor isassociated with a base station of at least part of a cellularcommunications network with which the antenna array forms part.
 54. Theapparatus of claim 45, wherein the performance metric is received from asimulator which simulates a physical node of at least part of acommunications network with which the antenna array forms part.
 55. Theapparatus of claim 54, wherein the simulated physical node is associateda base station of at least part of a cellular communications networkwith which the antenna array forms part.
 56. The apparatus of claim 54,wherein the computing processor or the simulator comprises a part of theapparatus.
 57. The apparatus of claim 45, wherein the receivedperformance metric represents the measured or simulated value of one ormore of data throughput, call drop rate, outage probability and energyconsumption associated with the antenna array.
 58. The apparatus ofclaim 57, wherein the performance metric represents said measured orsimulated values for each of a different number of user equipment withina cell of a cellular communications system associated with the antennaarray.
 59. The apparatus of claim 45, wherein the configurationparameters comprise one or more of, for a j^(th) radiating element of ani^(th) antenna of the antenna array: Position x_(i, j) ϵ 

 _(i, j)⊂ 

 ³ Azimuth angle φ_(i, j) ϵ[0, 2π] Elevation angle θ_(i, j)ϵ[−π/2, π/2]Analog phase shift β_(i, j)ϵ[−π, π] Analog signal gainα_(i, j)ϵ[α_(min), α_(max)]

and re-configuring the radiating configuration of the antenna arraycomprises updating one or more of said configuration parameters toeffect a corresponding change at one or more of the radiating elementsof the antenna array.
 60. The apparatus of claim 45, wherein there-configuring the radiating configuration of the antenna arraycomprises performing the re-configuring in substantially real-time ornear real-time based on the last-received value of the performancemetric.
 61. A method, comprising: receiving a performance metric for anantenna array comprised of a plurality of radiating elements, theantenna array having a radiating configuration represented byconfiguration parameters; updating the configuration parametersdependent on the received performance metric by estimating newconfiguration parameters for moving the performance metric towards atarget value; and re-configuring the radiating configuration of theantenna array based on the updated configuration parameters such thatthe physical geometry of the antenna array is changed.
 62. The method ofclaim 61, wherein the re-configuring comprises changing the physicalgeometry by moving one or more radiating elements based on there-configured configuration.
 63. The method of claim 61, furthercomprising iteratively updating the configuration parameters dependenton received performance metrics resulting from the updated configurationparameters, until a stop condition is reached.
 64. A non-transitorycomputer readable medium comprising program instructions stored thereonfor performing a method, comprising: receiving a performance metric foran antenna array comprised of a plurality of radiating elements, theantenna array having a radiating configuration represented byconfiguration parameters; updating the configuration parametersdependent on the received performance metric by estimating newconfiguration parameters for moving the performance metric towards atarget value; and re-configuring the radiating configuration of theantenna array based on the updated configuration parameters such thatthe physical geometry of the antenna array is changed.